{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flax Imagenet Example\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/google/flax/blob/main/examples/imagenet/imagenet.ipynb\" ><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "\n",
    "Demonstration notebook for\n",
    "https://github.com/google/flax/tree/main/examples/imagenet\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **Flax Notebook Workflow**:\n",
    "\n",
    "1. Run the entire notebook end-to-end and check out the outputs.\n",
    "   - This will open Python files in the right-hand editor!\n",
    "   - You'll be able to interactively explore metrics in TensorBoard.\n",
    "2. Change `config` and train for different hyperparameters. Check out the\n",
    "   updated TensorBoard plots.\n",
    "3. Update the code in `train.py`. Thanks to `%autoreload`, any changes you\n",
    "   make in the file will automatically appear in the notebook. Some ideas to\n",
    "   get you started:\n",
    "   - Change the model.\n",
    "   - Log some per-batch metrics during training.\n",
    "   - Add new hyperparameters to `configs/default.py` and use them in\n",
    "     `train.py`.\n",
    "4. At any time, feel free to paste code from `train.py` into the notebook\n",
    "   and modify it directly there!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "outputId": "cb862d1a-2f71-444f-9770-9f0d53b11389"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU 0: Tesla T4 (UUID: GPU-d1652fa8-88b9-3d02-7a65-e7ebabeb0372)\n"
     ]
    }
   ],
   "source": [
    "# Tested with a T4 GPU.\n",
    "!nvidia-smi -L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "outputId": "80340396-77c2-4654-cc6d-67040f227eb9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
     ]
    }
   ],
   "source": [
    "# Install ml-collections & latest Flax version from Github.\n",
    "!pip install -q clu ml-collections git+https://github.com/google/flax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "example_directory = 'examples/imagenet'\n",
    "editor_relpaths = ('configs/default.py', 'input_pipeline.py', 'models.py', 'train.py')\n",
    "\n",
    "repo, branch = 'https://github.com/google/flax', 'main'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "cellView": "form",
    "outputId": "9449a7b4-8a5d-4446-abe0-7886435ebd1c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h1 style=\"color:red;\" class=\"blink\">WARNING : Editing in VM - changes lost after reboot!!</h1>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "      ((filepath) => {{\n",
       "        if (!google.colab.kernel.accessAllowed) {{\n",
       "          return;\n",
       "        }}\n",
       "        google.colab.files.view(filepath);\n",
       "      }})(\"/content/examples/imagenet/configs/default.py\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "      ((filepath) => {{\n",
       "        if (!google.colab.kernel.accessAllowed) {{\n",
       "          return;\n",
       "        }}\n",
       "        google.colab.files.view(filepath);\n",
       "      }})(\"/content/examples/imagenet/input_pipeline.py\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "      ((filepath) => {{\n",
       "        if (!google.colab.kernel.accessAllowed) {{\n",
       "          return;\n",
       "        }}\n",
       "        google.colab.files.view(filepath);\n",
       "      }})(\"/content/examples/imagenet/models.py\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "      ((filepath) => {{\n",
       "        if (!google.colab.kernel.accessAllowed) {{\n",
       "          return;\n",
       "        }}\n",
       "        google.colab.files.view(filepath);\n",
       "      }})(\"/content/examples/imagenet/train.py\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# (If you run this code in Jupyter[lab], then you're already in the\n",
    "#  example directory and nothing needs to be done.)\n",
    "\n",
    "#@markdown **Fetch newest Flax, copy example code**\n",
    "#@markdown\n",
    "#@markdown **If you select no** below, then the files will be stored on the\n",
    "#@markdown *ephemeral* Colab VM. **After some time of inactivity, this VM will\n",
    "#@markdown be restarted and any changes are lost**.\n",
    "#@markdown\n",
    "#@markdown **If you select yes** below, then you will be asked for your\n",
    "#@markdown credentials to mount your personal Google Drive. In this case, all\n",
    "#@markdown changes you make will be *persisted*, and even if you re-run the\n",
    "#@markdown Colab later on, the files will still be the same (you can of course\n",
    "#@markdown remove directories inside your Drive's `flax/` root if you want to\n",
    "#@markdown manually revert these files).\n",
    "\n",
    "if 'google.colab' in str(get_ipython()):\n",
    "  import os\n",
    "  os.chdir('/content')\n",
    "  # Download Flax repo from Github.\n",
    "  if not os.path.isdir('flaxrepo'):\n",
    "    !git clone --depth=1 -b $branch $repo flaxrepo\n",
    "  # Copy example files & change directory.\n",
    "  mount_gdrive = 'no' #@param ['yes', 'no']\n",
    "  if mount_gdrive == 'yes':\n",
    "    DISCLAIMER = 'Note : Editing in your Google Drive, changes will persist.'\n",
    "    from google.colab import drive\n",
    "    drive.mount('/content/gdrive')\n",
    "    example_root_path = f'/content/gdrive/My Drive/flax/{example_directory}'\n",
    "  else:\n",
    "    DISCLAIMER = 'WARNING : Editing in VM - changes lost after reboot!!'\n",
    "    example_root_path = f'/content/{example_directory}'\n",
    "    from IPython import display\n",
    "    display.display(display.HTML(\n",
    "        f'<h1 style=\"color:red;\" class=\"blink\">{DISCLAIMER}</h1>'))\n",
    "  if not os.path.isdir(example_root_path):\n",
    "    os.makedirs(example_root_path)\n",
    "    !cp -r flaxrepo/$example_directory/* \"$example_root_path\"\n",
    "  os.chdir(example_root_path)\n",
    "  from google.colab import files\n",
    "  for relpath in editor_relpaths:\n",
    "    s = open(f'{example_root_path}/{relpath}').read()\n",
    "    open(f'{example_root_path}/{relpath}', 'w').write(\n",
    "        f'## {DISCLAIMER}\\n' + '#' * (len(DISCLAIMER) + 3) + '\\n\\n' + s)\n",
    "    files.view(f'{example_root_path}/{relpath}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "outputId": "acc1f45d-5062-4ff3-e6d4-10b4ffe0f8ef"
   },
   "outputs": [],
   "source": [
    "# Note : In Colab, above cell changed the working directory.\n",
    "!pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports / Helpers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "cellView": "form",
    "outputId": "9dc7fb32-331e-44a6-b6e8-830f6a64d845"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[StreamExecutorGpuDevice(id=0, process_index=0, slice_index=0)]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It's possible to run this Colab with TPUs:\n",
    "# 1. change runtime type to TPU\n",
    "# 2. install compatible flax version: `!pip install flax==0.6.4 jax==0.3.25 jaxlib==0.3.25`\n",
    "# 3. uncomment lines below\n",
    "\n",
    "# import flax, jax, jax.tools.colab_tpu\n",
    "# jax.tools.colab_tpu.setup_tpu()\n",
    "\n",
    "import jax\n",
    "jax.devices()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from absl import logging\n",
    "import flax\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import tensorflow_datasets as tfds\n",
    "\n",
    "logging.set_verbosity(logging.INFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper functions for images.\n",
    "\n",
    "def show_img(img, ax=None, title=None):\n",
    "  \"\"\"Shows a single image.\"\"\"\n",
    "  if ax is None:\n",
    "    ax = plt.gca()\n",
    "  img *= tf.constant(input_pipeline.STDDEV_RGB, shape=[1, 1, 3], dtype=img.dtype)\n",
    "  img += tf.constant(input_pipeline.MEAN_RGB, shape=[1, 1, 3], dtype=img.dtype)\n",
    "  img = np.clip(img.numpy().astype(int), 0, 255)\n",
    "  ax.imshow(img)\n",
    "  ax.set_xticks([])\n",
    "  ax.set_yticks([])\n",
    "  if title:\n",
    "    ax.set_title(title)\n",
    "\n",
    "def show_img_grid(imgs, titles):\n",
    "  \"\"\"Shows a grid of images.\"\"\"\n",
    "  n = int(np.ceil(len(imgs)**.5))\n",
    "  _, axs = plt.subplots(n, n, figsize=(3 * n, 3 * n))\n",
    "  for i, (img, title) in enumerate(zip(imgs, titles)):\n",
    "    show_img(img, axs[i // n][i % n], title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "outputId": "f943d165-b953-4a70-9f93-96eb857c3d53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# Local imports from current directory - auto reload.\n",
    "# Any changes you make to train.py will appear automatically.\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import input_pipeline\n",
    "import models\n",
    "import train\n",
    "from configs import default as config_lib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "outputId": "a9b6cfe9-cc1c-451a-f8f7-69356cb7bdd2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:No config specified, defaulting to config: imagenette/full-size-v2\n",
      "INFO:absl:Load dataset info from /root/tensorflow_datasets/imagenette/full-size-v2/1.0.0\n",
      "INFO:absl:Reusing dataset imagenette (/root/tensorflow_datasets/imagenette/full-size-v2/1.0.0)\n",
      "INFO:absl:Constructing tf.data.Dataset imagenette for split train, from /root/tensorflow_datasets/imagenette/full-size-v2/1.0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tfds.core.DatasetInfo(\n",
       "    name='imagenette',\n",
       "    full_name='imagenette/full-size-v2/1.0.0',\n",
       "    description=\"\"\"\n",
       "    Imagenette is a subset of 10 easily classified classes from the Imagenet\n",
       "    dataset. It was originally prepared by Jeremy Howard of FastAI. The objective\n",
       "    behind putting together a small version of the Imagenet dataset was mainly\n",
       "    because running new ideas/algorithms/experiments on the whole Imagenet take a\n",
       "    lot of time.\n",
       "    \n",
       "    This version of the dataset allows researchers/practitioners to quickly try out\n",
       "    ideas and share with others. The dataset comes in three variants:\n",
       "    \n",
       "    *   Full size\n",
       "    *   320 px\n",
       "    *   160 px\n",
       "    \n",
       "    Note: The v2 config correspond to the new 70/30 train/valid split (released in\n",
       "    Dec 6 2019).\n",
       "    \"\"\",\n",
       "    config_description=\"\"\"\n",
       "    full-size variant.\n",
       "    \"\"\",\n",
       "    homepage='https://github.com/fastai/imagenette',\n",
       "    data_path='/root/tensorflow_datasets/imagenette/full-size-v2/1.0.0',\n",
       "    file_format=tfrecord,\n",
       "    download_size=1.45 GiB,\n",
       "    dataset_size=1.46 GiB,\n",
       "    features=FeaturesDict({\n",
       "        'image': Image(shape=(None, None, 3), dtype=uint8),\n",
       "        'label': ClassLabel(shape=(), dtype=int64, num_classes=10),\n",
       "    }),\n",
       "    supervised_keys=('image', 'label'),\n",
       "    disable_shuffling=False,\n",
       "    splits={\n",
       "        'train': <SplitInfo num_examples=9469, num_shards=16>,\n",
       "        'validation': <SplitInfo num_examples=3925, num_shards=4>,\n",
       "    },\n",
       "    citation=\"\"\"@misc{imagenette,\n",
       "      author    = \"Jeremy Howard\",\n",
       "      title     = \"imagenette\",\n",
       "      url       = \"https://github.com/fastai/imagenette/\"\n",
       "    }\"\"\",\n",
       ")"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We load \"imagenette\" that has similar pictures to \"imagenet2012\" but can be\n",
    "# downloaded automatically and is much smaller.\n",
    "dataset_builder = tfds.builder('imagenette')\n",
    "dataset_builder.download_and_prepare()\n",
    "ds = dataset_builder.as_dataset('train')\n",
    "dataset_builder.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Utilities to help with Imagenette labels.\n",
    "\n",
    "![ ! -f mapping_imagenet.json ] && wget --no-check-certificate https://raw.githubusercontent.com/ozendelait/wordnet-to-json/master/mapping_imagenet.json\n",
    "\n",
    "with open('mapping_imagenet.json') as f:\n",
    "  mapping_imagenet = json.load(f)\n",
    "# Mapping imagenette label name to imagenet label index.\n",
    "imagenette_labels = {\n",
    "    d['v3p0']: d['label']\n",
    "    for d in mapping_imagenet\n",
    "}\n",
    "# Mapping imagenette label name to human-readable label.\n",
    "imagenette_idx = {\n",
    "    d['v3p0']: idx\n",
    "    for idx, d in enumerate(mapping_imagenet)\n",
    "}\n",
    "\n",
    "def imagenette_label(idx):\n",
    "  \"\"\"Returns a short human-readable string for provided imagenette index.\"\"\"\n",
    "  net = dataset_builder.info.features['label'].int2str(idx)\n",
    "  return imagenette_labels[net].split(',')[0]\n",
    "\n",
    "def imagenette_imagenet2012(idx):\n",
    "  \"\"\"Returns the imagenet2012 index for provided imagenette index.\"\"\"\n",
    "  net = dataset_builder.info.features['label'].int2str(idx)\n",
    "  return imagenette_idx[net]\n",
    "\n",
    "def imagenet2012_label(idx):\n",
    "  \"\"\"Returns a short human-readable string for provided imagenet2012 index.\"\"\"\n",
    "  return mapping_imagenet[idx]['label'].split(',')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "outputId": "78142300-cc8b-4a6c-f781-5ab29578d828"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Constructing tf.data.Dataset imagenette for split train[0:9469], from /root/tensorflow_datasets/imagenette/full-size-v2/1.0.0\n",
      "WARNING:absl:options.experimental_threading is deprecated. Use options.threading instead.\n",
      "INFO:absl:Constructing tf.data.Dataset imagenette for split validation[0:3925], from /root/tensorflow_datasets/imagenette/full-size-v2/1.0.0\n",
      "WARNING:absl:options.experimental_threading is deprecated. Use options.threading instead.\n"
     ]
    }
   ],
   "source": [
    "train_ds = input_pipeline.create_split(\n",
    "    dataset_builder, 128, train=True,\n",
    ")\n",
    "eval_ds = input_pipeline.create_split(\n",
    "    dataset_builder, 128, train=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "outputId": "8b3b9cf2-7649-4953-99bb-32a689fe0a29"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'image': (TensorShape([128, 224, 224, 3]), tf.float32),\n",
       " 'label': (TensorShape([128]), tf.int64)}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_batch = next(iter(train_ds))\n",
    "{k: (v.shape, v.dtype) for k, v in train_batch.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training from scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get a live update during training - use the \"refresh\" button!\n",
    "# (In Jupyter[lab] start \"tensorbaord\" in the local directory instead.)\n",
    "if 'google.colab' in str(get_ipython()):\n",
    "  %load_ext tensorboard\n",
    "  %tensorboard --logdir=."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "outputId": "2d0bc789-213d-4a34-a7b1-e7852b40f375"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "batch_size: 32\n",
       "cache: false\n",
       "dataset: imagenette\n",
       "half_precision: true\n",
       "learning_rate: 0.1\n",
       "log_every_steps: 100\n",
       "model: ResNet18\n",
       "momentum: 0.9\n",
       "num_epochs: 5.0\n",
       "num_train_steps: -1\n",
       "prefetch: 1\n",
       "shuffle_buffer_size: 128\n",
       "steps_per_eval: -1\n",
       "warmup_epochs: 0.5"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config = config_lib.get_config()\n",
    "config.dataset = 'imagenette'\n",
    "config.model = 'ResNet18'\n",
    "config.half_precision = True\n",
    "# Reduce batch size, shuffle buffer and prefetch to avoid Colab runtime OOM.\n",
    "config.batch_size = 32\n",
    "config.shuffle_buffer_size = 128\n",
    "config.prefetch = 1\n",
    "# Reduce epochs so this Colab doesn't take forever.\n",
    "config.num_epochs = 5.0\n",
    "config.warmup_epochs = 0.5\n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "outputId": "de56d320-c336-459b-f258-5d6ae41ce0af"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Constructing tf.data.Dataset imagenette for split train[0:9469], from /root/tensorflow_datasets/imagenette/full-size-v2/1.0.0\n",
      "WARNING:absl:options.experimental_threading is deprecated. Use options.threading instead.\n",
      "INFO:absl:Constructing tf.data.Dataset imagenette for split validation[0:3925], from /root/tensorflow_datasets/imagenette/full-size-v2/1.0.0\n",
      "WARNING:absl:options.experimental_threading is deprecated. Use options.threading instead.\n"
     ]
    }
   ],
   "source": [
    "# Regenerate datasets with updated batch_size.\n",
    "train_ds = input_pipeline.create_split(\n",
    "    dataset_builder, config.batch_size, train=True,\n",
    ")\n",
    "eval_ds = input_pipeline.create_split(\n",
    "    dataset_builder, config.batch_size, train=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "outputId": "018da2c5-c6f0-42ac-843f-7ac855a6bf14"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:No config specified, defaulting to config: imagenette/full-size-v2\n",
      "INFO:absl:Load dataset info from /root/tensorflow_datasets/imagenette/full-size-v2/1.0.0\n",
      "INFO:absl:Constructing tf.data.Dataset imagenette for split train[0:9469], from /root/tensorflow_datasets/imagenette/full-size-v2/1.0.0\n",
      "WARNING:absl:options.experimental_threading is deprecated. Use options.threading instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "ResNet18_lr=0.03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Constructing tf.data.Dataset imagenette for split validation[0:3925], from /root/tensorflow_datasets/imagenette/full-size-v2/1.0.0\n",
      "WARNING:absl:options.experimental_threading is deprecated. Use options.threading instead.\n",
      "INFO:absl:Restoring legacy Flax checkpoint from models/ResNet18_lr=0.03/checkpoint_1475\n",
      "INFO:absl:Initial compilation, this might take some minutes...\n",
      "INFO:absl:No config specified, defaulting to config: imagenette/full-size-v2\n",
      "INFO:absl:Load dataset info from /root/tensorflow_datasets/imagenette/full-size-v2/1.0.0\n",
      "INFO:absl:Constructing tf.data.Dataset imagenette for split train[0:9469], from /root/tensorflow_datasets/imagenette/full-size-v2/1.0.0\n",
      "WARNING:absl:options.experimental_threading is deprecated. Use options.threading instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "ResNet18_lr=0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Constructing tf.data.Dataset imagenette for split validation[0:3925], from /root/tensorflow_datasets/imagenette/full-size-v2/1.0.0\n",
      "WARNING:absl:options.experimental_threading is deprecated. Use options.threading instead.\n",
      "INFO:absl:Restoring legacy Flax checkpoint from models/ResNet18_lr=0.1/checkpoint_1475\n",
      "INFO:absl:Initial compilation, this might take some minutes...\n",
      "INFO:absl:No config specified, defaulting to config: imagenette/full-size-v2\n",
      "INFO:absl:Load dataset info from /root/tensorflow_datasets/imagenette/full-size-v2/1.0.0\n",
      "INFO:absl:Constructing tf.data.Dataset imagenette for split train[0:9469], from /root/tensorflow_datasets/imagenette/full-size-v2/1.0.0\n",
      "WARNING:absl:options.experimental_threading is deprecated. Use options.threading instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "ResNet18_lr=0.3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Constructing tf.data.Dataset imagenette for split validation[0:3925], from /root/tensorflow_datasets/imagenette/full-size-v2/1.0.0\n",
      "WARNING:absl:options.experimental_threading is deprecated. Use options.threading instead.\n",
      "INFO:absl:Restoring legacy Flax checkpoint from models/ResNet18_lr=0.3/checkpoint_1475\n",
      "INFO:absl:Initial compilation, this might take some minutes...\n"
     ]
    }
   ],
   "source": [
    "# Takes ~1.5 min / epoch.\n",
    "for lr in  (0.03, 0.1, 0.3):\n",
    "  config.learning_rate = lr\n",
    "  name = f'{config.model}_lr={config.learning_rate}'\n",
    "  print(f'\\n\\n{name}')\n",
    "  state = train.train_and_evaluate(config, workdir=f'./models/{name}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "cellView": "form"
   },
   "outputs": [],
   "source": [
    "if 'google.colab' in str(get_ipython()):\n",
    "  #@markdown You can upload the training results directly to https://tensorbaord.dev\n",
    "  #@markdown\n",
    "  #@markdown Note that everybody with the link will be able to see the data.\n",
    "  upload_data = 'no' #@param ['yes', 'no']\n",
    "  if upload_data == 'yes':\n",
    "    !tensorboard dev upload --one_shot --logdir ./models --name 'Flax examples/mnist'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load pre-trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "outputId": "b06fa3d8-a950-46d2-e03e-fc6c971bdbd0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r-- 1 root root 196M Apr  3 13:38 checkpoint_250200\n"
     ]
    }
   ],
   "source": [
    "# Load model checkpoint from cloud.\n",
    "from flax.training import checkpoints\n",
    "\n",
    "config_name = 'v100_x8'\n",
    "pretrained_path = f'gs://flax_public/examples/imagenet/{config_name}'\n",
    "latest_checkpoint = checkpoints.natural_sort(\n",
    "    tf.io.gfile.glob(f'{pretrained_path}/checkpoint_*'))[0]\n",
    "if not os.path.exists(os.path.basename(latest_checkpoint)):\n",
    "  tf.io.gfile.copy(latest_checkpoint, os.path.basename(latest_checkpoint))\n",
    "!ls -lh checkpoint_*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load config that was used to train checkpoint.\n",
    "import importlib\n",
    "config = importlib.import_module(f'configs.{config_name}').get_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "outputId": "57777298-4b4b-4a82-b0f2-4b6ff3b949af"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Restoring legacy Flax checkpoint from ./checkpoint_250200\n"
     ]
    }
   ],
   "source": [
    "# Load models & state (takes ~1 min to load the model).\n",
    "model_cls = getattr(models, config.model)\n",
    "model = train.create_model(\n",
    "    model_cls=model_cls, half_precision=config.half_precision)\n",
    "base_learning_rate = config.learning_rate * config.batch_size / 256.\n",
    "steps_per_epoch = (\n",
    "    dataset_builder.info.splits['train'].num_examples // config.batch_size\n",
    ")\n",
    "learning_rate_fn = train.create_learning_rate_fn(\n",
    "    config, base_learning_rate, steps_per_epoch)\n",
    "state = train.create_train_state(\n",
    "    jax.random.key(0), config, model, image_size=input_pipeline.IMAGE_SIZE,\n",
    "    learning_rate_fn=learning_rate_fn)\n",
    "state = train.restore_checkpoint(state, './')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "outputId": "793a656b-f3ad-4596-ad4f-44c686e5e885"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'image': TensorShape([32, 224, 224, 3]), 'label': TensorShape([32])}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load batch from imagenette eval set.\n",
    "batch = next(iter(eval_ds))\n",
    "{k: v.shape for k, v in batch.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate using model trained on imagenet.\n",
    "logits = model.apply({'params': state.params, 'batch_stats': state.batch_stats}, batch['image'][:128], train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "outputId": "6ab4bb0b-2c03-4663-d7ac-e51b979d121f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[16, 24]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find classification mistakes.\n",
    "preds_labels = list(zip(logits.argmax(axis=-1), map(imagenette_imagenet2012, batch['label'])))\n",
    "error_idxs = [idx for idx, (pred, label) in enumerate(preds_labels) if pred != label]\n",
    "error_idxs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "outputId": "142c1acf-037e-4ab0-9ca3-bdf0829c51c4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The mistakes look all quite reasonable.\n",
    "show_img_grid(\n",
    "    [batch['image'][idx] for idx in error_idxs[:9]],\n",
    "    [f'pred: {imagenet2012_label(preds_labels[idx][0])}\\n'\n",
    "     f'label: {imagenet2012_label(preds_labels[idx][1])}'\n",
    "    for idx in error_idxs[:9]],\n",
    ")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "outputId": "4fae2533-5598-4f2e-c133-50bfba463311"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Constructing tf.data.Dataset imagenette for split validation[0:3925], from /root/tensorflow_datasets/imagenette/full-size-v2/1.0.0\n",
      "WARNING:absl:options.experimental_threading is deprecated. Use options.threading instead.\n"
     ]
    }
   ],
   "source": [
    "# Define parallelized inference function in separate cell so the cached\n",
    "# compilation can be used if below cell is executed multiple times.\n",
    "@jax.pmap\n",
    "def p_get_logits(images):\n",
    "  return model.apply({'params': state.params, 'batch_stats': state.batch_stats},\n",
    "                     images, train=False)\n",
    "\n",
    "eval_iter = train.create_input_iter(dataset_builder, config.batch_size,\n",
    "                                    input_pipeline.IMAGE_SIZE, tf.float32,\n",
    "                                    train=False, cache=False, shuffle_buffer_size=None,\n",
    "                                    prefetch=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "outputId": "d01e1993-28ab-4a4a-ac58-01c83b80e6c9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 1/7...\n",
      "Step 2/7...\n",
      "Step 3/7...\n",
      "Step 4/7...\n",
      "Step 5/7...\n",
      "Step 6/7...\n",
      "Step 7/7...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Array(0.9118304, dtype=float32)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute accuracy.\n",
    "eval_steps = dataset_builder.info.splits['validation'].num_examples // config.batch_size\n",
    "count = correct = 0\n",
    "for step, batch in zip(range(eval_steps), eval_iter):\n",
    "  labels = [imagenette_imagenet2012(label) for label in batch['label'].flatten()]\n",
    "  logits = p_get_logits(batch['image'])\n",
    "  logits = logits.reshape([-1, logits.shape[-1]])\n",
    "  print(f'Step {step+1}/{eval_steps}...')\n",
    "  count += len(labels)\n",
    "  correct += (logits.argmax(axis=-1) == jnp.array(labels)).sum()\n",
    "\n",
    "correct / count"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "gpuClass": "standard",
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
